|
|
Novel dynamic mapping method based on occupancy grid model and sample sets |
CHEN Jia-qian1, LIUYu-tian2, HE Yan1, JIANG Jing-ping1 |
1. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; 2. Faculty of Electronic and Information Engineering, Zhejiang Wanli University, Ningbo 315100, China |
|
|
Abstract Though the occupancy grid map could be used to express the dynamic environment, the multiple timescale maps is often needed, and it is difficult to describe the dynamic information. To overcome these difficulties, a novel environment model combining the occupancy grid model and sample sets is proposed. In this model, the static parts of the environment are estimated by the occupancy grid model with Bayesian method. The dynamic objects are described by a current sample set of current positions and a history sample set of sensor measurements. By fusing the sample sets and the occupancy grid model, the environment maps could demonstrate not only the dynamic objects' positions but also their major active regions. An experiment was carried out in the dynamic laboratory environment. The results showed that the proposed method could build accurate and useful maps of dynamic environments and would facilitate the path planning and navigation.
|
Published: 24 November 2011
|
|
基于栅格模型和样本集合的动态环境地图创建
采用单一栅格模型创建动态环境的地图,往往需要构建多个时间尺度的地图,且无法有效表述环境中的动态信息.为了克服上述困难,提出一种用栅格模型和样本集合创建动态环境地图的方法.栅格模型对静态障碍物用贝叶斯方法进行置信概率估计、当前样本集合迭代更新动态障碍物当前所处位置、历史样本集合保存动态障碍物的所有历史传感信息.通过将样本集合与栅格模型融合,可以有效表征动态障碍物的当前位置和主要活动区域.实验室动态环境下的实验结果表明:该算法能够构建信息完整且精度较高的动态环境地图,为后续路径规划和导航提供便利.
|
|
[1] MITSOU N C, TZAFESTAS C S. An introduction to the problem of mapping in dynamic environments[EB/OL]. [20090328]. http:∥www.books.itechonline.com/downloadpdf.php?id=5293. [2] LIMKETKAI B, BISWAS R, THRUN S. Learning occupancy grids of nonstationary objects with mobile robots [C]∥ Springer Tracts in Advanced Robotics.Berlin/Heidelberg, German: Springer Press, 2002: 222-231. [3] STACHNISS C. Exploration and mapping with mobile robots [D]. Freiburg, German: Department of Computer Science, University of Freiburg, 2006. [4] HAHNEL D. Mapping with mobile robots[D]. Freiburg, German: Department of Computer Science, University of Freiburg, 2004. [5] WANG C C, THORPE C, THRUN S. Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas[C]∥ Proceedings of IEEE International Conference on Robotics and Automation. Taipei: IEEE, 2003: 842-849. [6] WOLF D F, SUKHATME G S. Mobile robot simultaneous localization and mapping in dynamic environments[J]. Autonomous Robots, 2005, 19(1): 53-65. [7] ARBUCKLE D, HOWARD A, MATARIC M J. Temporal occupancy grids: a method for classifying spatiotemporal properties of the environment [C]∥ Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.Lausanne, Switzerland: IEEE, 2002: 409-414. [8] BIBER P, DUCKETT T. Dynamic maps for longterm operation of mobile service robots [C]∥ Robotics: Science and Systems I. Cambridge,Massachusetts,USA:MIT, 2005: 17-24. [9] MITSOU N C, TZAFESTAS C S. Temporal occupancy grid for mobile robot dynamic environment mapping [C]∥ Proceedings of IEEE Mediterranean Conference on Control and Automation.Athens, Greece: IEEE, 2007: 1-8. [10] LEAL J. Stochastic environment representation[D]. Sydney: Australia, School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, 2003. [11] BESL P J, MCKAY N D. A method for registration of 3D shapes [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 239-256. [12] THRUN S, BUGARD W, FOX D. Probabilistic robotics [M]. MA, USA: MIT, 2005: 281-299. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|